SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Fabiana Santos(fabianasantos.mestrado@gmail.com)
2 Lucas Tavares(lucas.giusti@eic.cefet-rj.br)
3 Diego Carvalho(d.carvalho@ieee.org)
4 Eduardo Ogasawara( eogasawara@ieee.org)
5 Jorge Soares(jorge@eic.cefet-rj.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 ANAC (2023). The Brazilian National Civil Aviation Agency. Technical report, http://www.anac.gov.br/.
2 Gama, J., Medas, P., Castillo, G., and Rodrigues, P. P. (2004). Learning with drift detection. In Brazilian Symposium on Artificial Intelligence (SBIA), pages 286–295. Springer.
3 Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4).
4 Giusti, L., Carvalho, L., Gomes, A. T., Coutinho, R., Soares, J., and Ogasawara, E. (2022). Analyzing flight delay prediction under concept drift. Evolving Systems.
5 Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., and Zhao, D. (2020). Flight delay prediction based on aviation big data and machine learning. IEEE Transactions on Vehicular Technology, 69(1):140 – 150.
6 Iwashita, A. S. and Papa, J. P. (2019a). An Overview on Concept Drift Learning. IEEE Access, 7:1532 – 1547.
7 Iwashita, R. and Papa, J. P. (2019b). An Overview on Concept Drift Adaptation. Journal of Artificial Intelligence Research.
8 Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2019). Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering, 31(12):2346 – 2363.
9 Moreira, L., Dantas, C., Oliveira, L., Soares, J., and Ogasawara, E. (2018). On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays. In Proceedings of the International Joint Conference on Neural Networks, volume 2018-July.
10 NOAA (2023). Climate at a Glance Global Time Series. Technical report, https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series.
11 Raab, C., Heusinger, M., and Schleif, F.-M. (2020). Reactive soft prototype computing for concept drift streams. Neurocomputing.
12 Rani, S. S., Ali, A. I. A., Marie, A., El-Bannany, M., and Khedr, A. M. (2023). Air Traffic Data Analysis Using Recurrent Neural Network (RNN) Classifier During COVID19. In Proceedings - 17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023, pages 402 – 408.
13 Sakthithasan, S. and Pears, R. (2016). Capturing recurring concepts using discrete Fourier transform. Concurrency and Computation: Practice and Experience, 28(15):4013 – 4035.
14 Teixeira, C., Giusti, L., Soares, J., dos Santos, J., Amorim, G., and Ogasawara, E. (2021). Integrated Dataset of Brazilian Flights. In Anais do Brazilian e-Science Workshop (BreSci), pages 89–96. SBC.
15 Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., and Petitjean, F. (2016). Characterizing concept drift. Data Mining and Knowledge Discovery, 30(4):964 – 994.